AI-300: MLOps Engineer Associate
Operationalise machine learning & GenAIOps on Azure
Take models from prototype to production with secure, scalable MLOps practices and automation tooling. Learn how to build end-to-end GenAIOps pipelines using Azure Machine Learning, Microsoft Foundry and GitHub Actions while ensuring quality, observability and compliance with Australian frameworks.
At a Glance
Who it's for
- ML engineers and data scientists operationalising models
- DevOps/Platform engineers building AI infrastructure
- AI Ops teams responsible for monitoring and governance
- Cloud architects designing secure AI platforms
- Developers preparing for the Microsoft AI-300 certification
- Organisations automating generative AI workflows
Course Details
Course Overview
This 24-hour intermediate course teaches you how to take ML and generative AI workloads from research to production on Azure. You'll build secure, scalable AI environments using Bicep and GitHub Actions, manage model lifecycles in Azure Machine Learning, and automate GenAIOps pipelines with Microsoft Foundry. Quality assurance, observability and performance optimisation are emphasised alongside responsible AI practices tied to Australian Privacy Principles, Essential Eight and ISO 27001. Ideal for engineers, ops teams and organisations looking to embed MLOps principles within their AI delivery processes.
What You'll Learn
Course Curriculum
Module 1: AI Operations (AIOps) Infrastructure
5 hours- Designing secure AI network topologies and private endpoints
- Provisioning resources with Bicep and Azure CLI
- Automating deployments via GitHub Actions
- Configuring managed identities and key vault integration
- Cost management and resource tagging practices
- Aligning infrastructure to Essential Eight and ISO 27001
Module 2: ML Model Lifecycle Management
6 hours- Registering and versioning models in Azure Machine Learning
- Creating pipelines for training and validation
- Progressive deployment strategies and blue/green rollouts
- Tracking lineage and metadata for auditability
- Managing datasets and feature stores
- Governance controls and access policies
Module 3: GenAIOps Infrastructure
5 hours- Introduction to Microsoft Foundry for generative workloads
- Building end-to-end automation pipelines
- Integrating LLMs with external data sources and APIs
- Versioning prompts and evaluation artefacts
- Scheduling and error handling in workflows
- Scaling GenAI services for production use
Module 4: Quality Assurance & Observability
4 hours- Implementing Promptflow tracing and test suites
- Setting up safety evaluation jobs and scoring
- Monitoring data and model drift with Azure Monitor
- Configuring alerts and dashboards
- Logging strategies for generative AI outputs
- Compliance reporting for APPs and audit requirements
Module 5: Performance Optimisation
4 hours- Tuning retrieval-augmented generation (RAG) pipelines
- Fine-tuning and adapting embedding models
- Using synthetic data to improve training velocity
- Resource scaling and GPU utilisation best practice
- Cost-effective model hosting and inference strategies
Who Should Attend
- ML engineers, data scientists and AI Ops practitioners
- DevOps and platform engineers supporting AI workloads
- Cloud architects and solution designers
- Developers building generative AI applications
- IT professionals responsible for compliance and security
- Teams preparing for AI-300 qualification
Prerequisites
Before enrolling, please ensure you meet these requirements:
- • Basic understanding of Azure fundamentals or completion of AI-901
- • Experience writing scripts in Python, PowerShell or similar
- • Familiarity with Git, CLI tooling and YAML pipelines
- • General knowledge of machine learning lifecycle concepts
- • Access to an Azure subscription (free tier is acceptable)
Delivery, Format and Logistics
Delivery Mode
Live online with hands-on labs
Maximum 16 participants for intensive hands-on labs
What You'll Need
- Computer with modern web browser and development environment
- Active Microsoft Azure subscription (free tier acceptable)
- Visual Studio Code or preferred IDE installed
- Reliable internet connection (minimum 15 Mbps)
- Basic experience with Python or PowerShell (or equivalent)
- Familiarity with Git, Azure CLI and YAML pipelines helpful
- Understanding of machine learning concepts beneficial
What You'll Receive
- 24 hours of instructor-led training over three days
- Practical labs using real Azure services and Microsoft Foundry
- Course workbook, templates and code samples
- Exam-style scenarios for AI-300 certification preparation
- Responsible AI & compliance checklists
- Certificate of completion
- 6 months access to materials
- Invitation to private Q&A community
Frequently Asked Questions
Not Ready to Enrol?
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Early-bird rate — apply your promo code at checkout.
Secure payment via Stripe · Promo codes accepted
Next Intake
September 2026 — register your interest at educ4te.com
Format
Live online with hands-on labs
Group & Enterprise Options
Discounted rates available for teams of 3+ delegates. Contact us for in-house delivery options.
What's Included
- 24 hours of instructor-led training over three days
- Practical labs using real Azure services and Microsoft Foundry
- Course workbook, templates and code samples
- Exam-style scenarios for AI-300 certification preparation
- Responsible AI & compliance checklists
- Certificate of completion
- 6 months access to materials
- Invitation to private Q&A community
Have questions about this course?